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Efficient similarity joins for near duplicate detection
Abstract
With the increasing amount of data and the need to integrate data from multiple data sources, a challenging issue is to find near duplicate records efficiently. In this paper, we focus on efficient algorithms to find pairs of records such that their similarities are above a given threshold. Several existing algorithms rely on the prefix filtering principle to avoid computing similarity values for all possible pairs of records. We propose new filtering techniques by exploiting the ordering information; they are integrated into the existing methods and drastically reduce the candidate sizes and hence improve the efficiency. Experimental results show that our proposed algorithms can achieve up to 2.6x - 5x speed-up over previous algorithms on several real datasets and provide alternative solutions to the near duplicate Web page detection problem.
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Cites
Approximate nearest neighbors: towards removing the curse of dimensionality
Indyk, P., & Motwani, R. (1998) · Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98 · doi:10.1145/276698.276876 · Cited by 4,188
Metadata
- Authors
- Xiao, C., Wang, W., Lin, X., & Yu, J. X.
- Venue
- Proceedings of the 17th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2008-04-21
- Pages
- 131-140
- OpenAlex
- W2097184821
- Cited by
- 455
- References
- 1
- FWCI
- 58.82
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Efficient similarity joins for near duplicate detection. https://app.cochat.ai/papers/10.1145/1367497.1367516
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- Xiao, C., Wang, W., Lin, X., & Yu, J. X. (2008). Efficient similarity joins for near duplicate detection. Proceedings of the 17th international conference on World Wide Web. https://doi.org/10.1145/1367497.1367516
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
